The freeze-thaw cycle of near-surface soils significantly affects energy and water exchanges between the atmosphere and land surface. Passive microwave remote sensing is commonly used to observe the freeze-thaw state. However, existing algorithms face challenges in accurately monitoring near-surface soil freeze/thaw in alpine zones. This article proposes a framework for enhancing freeze/thaw detection capability in alpine zones, focusing on band combination selection and parameterization. The proposed framework was tested in the three river source region (TRSR) of the Qinghai-Tibetan Plateau. Results indicate that the framework effectively monitors the freeze/thaw state, identifying horizontal polarization brightness temperature at 18.7 GHz (TB18.7H) and 23.8 GHz (TB23.8H) as the optimal band combinations for freeze/thaw discrimination in the TRSR. The framework enhances the accuracy of the freeze/thaw discrimination for both 0 and 5-cm soil depths. In particular, the monitoring accuracy for 0-cm soil shows a more significant improvement, with an overall discrimination accuracy of 90.02%, and discrimination accuracies of 93.52% for frozen soil and 84.68% for thawed soil, respectively. Furthermore, the framework outperformed traditional methods in monitoring the freeze-thaw cycle, reducing root mean square errors for the number of freezing days, initial freezing date, and thawing date by 16.75, 6.35, and 12.56 days, respectively. The estimated frozen days correlate well with both the permafrost distribution map and the annual mean ground temperature distribution map. This study offers a practical solution for monitoring the freeze/thaw cycle in alpine zones, providing crucial technical support for studies on regional climate change and land surface processes.

The Tibetan Plateau (TP) is a region rich in extensive frozen ground and the source of many major Asian rivers. However, how soil freeze/thaw (F/T) dynamics influence runoff production at the catchment scale in the TP is poorly understood. This study employs a process-based permafrost hydrology model with a new soil parameterization to investigate soil F/T dynamics and their impact on runoff in a TP permafrost watershed, i.e., the source region of Yangtze River (SRYR). The revised model separates soil evaporation and plant transpiration, and accounts for the influence of soil gravel and organic carbon content, as well as variation in saturated hydraulic conductivity along the soil profile. Validation results demonstrate that the revised model accurately simulates daily soil temperature (mean RMSE of 1.3 degrees C), soil moisture (mean ubRMSE of 0.05 cm3 cm-3), and runoff discharge (NSE = 0.82). The results reveal different altitudinal patterns of warming trend between permafrost and seasonally frozen ground (SFG). Warming rates in SFG area increase monotonously with elevation, while a turning point is observed in permafrost region around 4800 m. With active layer deepening, deep-soil water content increases but primarily replenishes soil water storage rather than directly contributing to runoff recharge, while rootzone and the middle part of the active layer become drier. Soil F/T cycles in the permafrost region exert stronger influences on runoff process compared to SFG. Delayed soil thaw onset generally results in higher spring runoff coefficient, while delayed soil freeze onset is related to slower runoff recession. The freezing zero-curtain period is likely to impact the continuity of runoff recession processes by affecting the connectivity of groundwater flow channels. These findings uncover the regulatory mechanisms of soil F/T dynamics on runoff production and river discharge characteristics, providing a fundamental basis for predicting permafrost hydrology responses to future climate change in the TP.

Permafrost warming has been observed all around the Arctic, however, variations in temperature trends and their drivers remain poorly understood. We present a comprehensive analysis of climatic changes spanning 25 years (1998-2023) at Bayelva (78.92094 degrees N, 11.83333 degrees E) on Spitzbergen, Svalbard. The quality controlled hourly data set includes air temperature, radiation fluxes, snow depth, rainfall, active layer temperature and moisture, and, since 2009, permafrost temperature. Our Bayesian trend analysis reveals an annual air temperature increase of 0.9 +/- 0.5 degrees C/decade and strongest warming in September and October. We observed a significant shortening of the snow cover by -14 +/- 8 days/decade, coupled with reduced winter snow depth. The active layer simultaneously warmed by 0.6 +/- 0.7 degrees C/decade at the top and 0.8 +/- 0.5 degrees C/decade at the bottom. While the soil surface got drier, in particular during summer, soil moisture below increased in accordance with the longer unfrozen period and higher winter temperatures. The thawed period prolonged by 10-15 days/decade at different depths. In contrast to earlier top-soil warming, we observed stable temperatures since 2010 and only little permafrost warming (0.14 +/- 0.13 degrees C/decade). This is likely due to recently stable winter air temperature and continuously decreasing winter snow depth. This recent development highlights a complex interplay among climate and soil variables. Our distinctive long-term data set underscores (a) the changes in seasonal warming patterns, (b) the influential role of snow cover decline, and (c) that air temperature alone is not a sufficient indicator of change in permafrost environments, thereby highlighting the importance of investigating a wider range of parameters, such as soil moisture and snow characteristics. Permafrost is warming across the Arctic, but it is not yet well understood why temperature trends vary and what affects them the most. Our detailed study investigates 25 years (1998-2023) of data at the Bayelva permafrost observatory on Svalbard. We analyzed a quality-controlled data set, including hourly measurements of air temperature, radiation, snow depth, rainfall, permafrost temperature, and active layer conditions. We looked beyond annual averages, examining changes in each month. The air warmed strongly by 0.9 degrees C/decade and even stronger in September and October. Continuous snow cover shortened by -14 days/decade and winter snow depth decreased. Simultaneously, the active layer warmed by 0.6 degrees C/decade at the top and 0.8 degrees C/decade at the bottom. While the surface dried in summer, deeper soil layers became moister due to a longer unfrozen period and higher temperatures in winter. The thawed period extended by 10-15 days/decade, with slightly stronger changes toward later freezing in autumn. We found that soil warming stopped in recent years and attributed this effect to the lower winter snow depth since 2010. Therefore, if we want to know how permafrost warms or cools in the future, we need to consider additional measurements such as soil moisture and snow properties. Snow cover thinning and winter air temperature variability are the most important drivers of trends in permafrost temperature While mean annual air temperature continues to increase, the top soil at Bayelva, Svalbard, stopped warming since 2010 The fully snow-covered season shortened by -14 days per decade since 1998, which led to longer unfrozen conditions in the active layer

The soil freeze/thaw (FT) state has emerged as a critical role in the ecosystem, hydrological, and biogeochemical processes, but obtaining representative soil FT state datasets with a long time sequence, fine spatial resolution, and high accuracy remains challenging. Therefore, we propose a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) to expand the SMAP-enhanced L3 landscape freeze/thaw product (SMAP_E_FT) temporally. In the algorithm, the Freeze/Thaw Earth System Data Record product (ESDR_FT) is sucked in the ConvLSTM and fused with SMAP_E_FT at the decision level. Eight predictor datasets, i.e., soil temperature, snow depth, soil moisture, precipitation, terrain complexity index, area of open water data, latitude and longitude, are used to train the ConvLSTM. Direct validation using six dense observation networks located in the Genhe, Maqu, Naqu, Pali, Saihanba, and Shandian river shows that the fusion product (ConvLSTM_FT) effectively absorbs the high accuracy characteristics of ESDR_FT and expands SMAP_E_FT with an overall average improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (averagely improved by 7.03%). The result from indirect validation based on categorical triple collocation also shows that ConvLSTM_FT performs stable regardless of land cover types, climate types, and terrain complexity. The findings, drawn from preliminary analyses on ConvLSTM_FT from 1980 to 2020 over China, suggest that with global warming, most parts of China suffer from different degrees of shortening of the frozen period. Moreover, in the Qinghai-Tibet region, the higher the permafrost thermal stability, the faster the degradation rate.

The freeze-thaw (F/T) process plays a significant role in climate change and ecological systems. The soil F/T state can now be determined using microwave remote sensing. However, its monitoring capacity is constrained by its low spatial resolution or long revisit intervals. In this study, spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data with high temporal and spatial resolutions were used to detect daily soil F/T cycles, including completely frozen (CF), completely thawed (CT), and F/T transition states. First, the calibrated Cyclone Global Navigation Satellite System (CYGNSS) reflectivity was used for soil F/T classification. Compared with those of soil moisture active and passive (SMAP) F/T data and in situ data, the detection accuracies of CYGNSS reach 75.1% and 81.4%, respectively. Subsequently, the changes in spatial characteristics were quantified, including the monthly occurrence days of the soil F/T state. It is found that the CF and CT states have opposite spatial distributions, and the F/T transition states distribute from the east to the west and then back to the east of the Qinghai-Tibet Plateau, which may be due to varying diurnal temperatures in different seasons. Finally, the first day of thawing (FDT), last day of thawing, and thawing period of the F/T year were analyzed in terms of the changes in temporal characteristics. The temporal variation of thawing is mainly different between the western and eastern parts of the Tibetan Plateau, which is in agreement with the spatial variation characteristics. The results demonstrate that the CYGNSS can accurately detect the F/T state of near-surface soil on a daily scale. Moreover, it can complement traditional remote sensing missions to improve the F/T detection capability. It can also expand the applications of GNSS-R technology and provide new avenues for cryosphere research.

In the context of global warming, permafrost degrades gradually. To cope with the instability of the cryosphere, it is very important to strengthen the monitoring of the seasonal freeze-thaw cycle. At present, active and passive microwave remote sensing data are widely used in freeze/thaw (F/T) onset detection. There is some potential to improve accuracy through the combination of active and passive microwave data. Compared with the traditional method for combination, the machine learning algorithm has a stronger nonlinear expression ability. Therefore, it is advisable to use machine learning to combine multi-source data for freeze/thaw onset detection. In this study, the temporal change detection method is applied to SMAP data and ASCAT data respectively for preliminary detection. Then the Random Forest algorithm (RF) is used to combine the preliminary results of active and passive microwave data with site observation to estimate the freeze/thaw onsets more accurately. The method is validated with data obtained in Alaska from 2015 to 2019. The accuracy evaluation shows that the proposed method can effectively improve the accuracy of freeze/thaw onset detection. The predicted distribution of the freeze/thaw cycle indicates that the variation of the freeze-thaw cycle is closely related to latitude. In general, the proposed method based on machine learning is promising in the research of freeze-thaw state monitoring.

Permafrost monitoring using remote sensing techniques is an effective approach at present. Permafrost mostly occurs below the land surface, which limits permafrost monitoring by optical remote sensing. Considering the specific hydrothermal relations between permafrost and its active layer, we developed a permafrost monitoring and classification method that integrated the ground surface soil freeze/thaw states determined by the dual-index algorithm (DIA) and the permafrost classification method based on thermal stability. The modified frost index was introduced into the method as a link between the DIA and the permafrost classification method. Northeastern China was selected to establish and verify the proposed method and to examine the changes in regional permafrost against the background of global warming from 2002 to 2017. The results showed that the ground surface soil freeze/thaw states were significantly correlated with the permafrost distribution. The spatial continuity of permafrost and its sensitivity to climate change could be effectively reflected by the modified frost index. The proposed method had a high accuracy with a classification error smaller than 3%, compared with static permafrost maps. Moreover, the proportion of permafrost decreased from 29% at the beginning of the 21st century to 22.5% at present in northeastern China over the study period. The southern permafrost boundary in the study area generally moved northward approximately 25-75 km. Additionally, the method was applied to the Northern Hemisphere (30 degrees N - 90 degrees N), which demonstrated its effectiveness and extended applicability.

InSAR time series of surface deformation from 16 yr of Envisat (2003-2011) and Sentinel-1 (2014-2019) ESA satellite radar measurements have been constructed to characterise spatial and temporal dynamics of ground deformation over an 80,000 km(2) area in the permafrost of the northeastern Tibetan Plateau. The regional deformation maps encompass various types of periglacial landforms and show that seasonal thaw effects are controlled by the sediment type and local topography. High seasonal ground movements are concentrated on shallow slopes and poor-drainage areas in unconsolidated, frost-susceptible and fine-grained sediments within glacier outwash plains, braided stream plains, alluvial deposits or floodplains. Fast subsidence due to thaw settlement takes place during June/July while frost heave is intense during December/January when two-sided freezing of pore water under pressure causes prolonged ice segregation near the permafrost table. The analysis reveals pervasive subsidence of the ground of up to similar to 2 cm/yr, and increasing by a factor of 2 to 5 from 2003 to today, in high-relief and well-drained areas. The findings suggest that seasonal thaw increasingly affects ice-rich layers at the permafrost table, as well as high-rates of widespread mass movements of non-consolidated sediments, the latter amplified by an increase of effects from frost heave/thaw settlement. (C) 2020 Elsevier B.V. All rights reserved.

The surface seasonal freeze/thaw (F/T) signal detected by passive microwave remote sensing is very important for the water cycle, carbon cycle and climate change research. In this study, we evaluated and analyzed the Soil Moisture Active Passive (SMAP) L3 F/T product, Advanced Microwave Scanning Radiometer 2 (AMSR2) F/T product and Making Earth System Data Records for Use in Research Environments (MEaSUREs) F/T product over different regions in China, including the Genhe area in Northeast China, the Saihanba area in North China, and the Qinghai-Tibet Plateau (QTP) area. The overall accuracy of F/T products assessed with the 5 cm depth soil temperature is 90.38% for SMAP, 90.23% for AMSR2 and 84.73% for MEaSUREs in cold and humid temperate forest climates and the plateau continental climate area (Genhe, Tianjun, and Qumalai) where permafrost is distributed, and 76.64% for SMAP, 83.67% for AMSR2 and 77.37% for MEaSUREs in the cold plateau mountain climate and plateau continental climate area (Saihanba and Chengduo) with frozen ground distributed seasonally, respectively. The overall accuracy is 69.05% for SMAP, 76.5% for AMSR2 and 81.4% for MEaSUREs in the Ngari, Naqu, and Dachaidan regions belonging to arid and semi-arid climates. It can be seen that SMAP and AMSR2 achieve the best performance in the distributed permafrost area, the second-best performance in the seasonal distributed permafrost area, but the worst performance in the areas with arid and semi-arid climate types due to inconsistent F/T signals between water with small changes and temperature with apparent changes during the F/T transition. The MEaSUREs product showed almost the same performance in different regions, indicating that it was less affected by climate types and the distribution of frozen soil than SMAP and AMSR2 products. SMAP F/T product detected by L-band with long penetration and AMSR2 F/T product calibrated with 5 cm soil temperature could represent the 5 cm F/T, but the MEaSUREs F/T product was more likely to describe the surface F/T state due to calibrated with air temperature and the short penetration of 36.5 GHz. In mid-low latitude areas (Tianjun and Qumalai) with a short duration of snow cover days and a fast snowmelt, the effect of snow melting on F/T products was negligible. Moreover, the spring snowmelt affects the three F/T products in Chengduo, but the SMAP product is not affected by the winter snowmelt, whereas the AMSR2 product is affected by the winter snowmelt.

Currently, the community lacks capabilities to assess and monitor landscape scale permafrost active layer dynamics over large extents. To address this need, we developed a concept of a remote sensing based Soil Inversion Model for regional Permafrost (SIM-P) monitoring. The current SIM-P framework includes a satellite-based soil process model and a soil dielectric model. We are also working on incorporating a radar scattering model for Arctic tundra into the SIM-P framework. A unified soil parameterization scheme was developed to harmonize key soil thermal, hydraulic and dielectric parameters in the soil process and radar models that can be used in the joint soil-radar inversion framework. The soil parameter retrievals of the SIM-P framework include soil organic content (SOC) and active layer thickness (ALT). Initial tests of SIM-P using in-situ soil permittivity observations showed reasonable accuracy in predicting site-level SOC and soil temperature profiles at an Alaska tundra site and ALT in Arctic Alaska. SIM-P will be further tested using airborne P- and L-band radar data collected during NASA's Arctic Boreal Vulnerability Experiment (ABoVE) to evaluate the sensitivity of longwave radar to active layer properties.